# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# Imports
import logging
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from npu_bridge.estimator.npu.npu_config import NPURunConfig
from npu_bridge.estimator.npu.npu_config import ProfilingConfig
from npu_bridge.estimator.npu.npu_estimator import NPUEstimator
from npu_bridge.estimator.npu.npu_optimizer import NPUDistributedOptimizer

"""
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/layers/cnn_mnist.py
python3 mnist_with_estimator.py
"""

# Model specific parameters
tf.flags.DEFINE_string("data_dir", "/tmp/mnist/MNIST-data",
                       "Path to directory containing the MNIST dataset")
tf.flags.DEFINE_string("model_dir", "/tmp/mnist/mnist_convnet_model", "Estimator model_dir")

tf.flags.DEFINE_integer("batch_size", 100,
                        "Mini-batch size for the training. Note that this "
                        "is the global batch size and not the per-shard batch.")
tf.flags.DEFINE_integer("train_steps", 1, "Total number of training steps.")
tf.flags.DEFINE_integer("eval_steps", 1,
                        "Total number of evaluation steps. If `0`, evaluation "
                        "after training is skipped.")
tf.flags.DEFINE_float("learning_rate", 0.001, "Learning rate.")

tf.flags.DEFINE_string("local_log_dir", "/tmp/mnist/train_logs.txt",  "Log file path")
tf.flags.DEFINE_integer("iterations", 50,
                        "Number of iterations per TPU training loop.")

tf.flags.DEFINE_string("job_start_file", "/tmp/config/deviceid_devindex_jobstart",
                       "CSA job start file path.")

FLAGS = tf.flags.FLAGS

def create_model(features, mode):
  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
  # Convolutional Layer #1
  # Computes 32 features using a 5x5 filter with ReLU activation.
  # Padding is added to preserve width and height.
  # Input Tensor Shape: [batch_size, 28, 28, 1]
  # Output Tensor Shape: [batch_size, 28, 28, 32]
  conv1 = tf.layers.conv2d(
      inputs=input_layer,
      filters=32,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)
  # Pooling Layer #1
  # First max pooling layer with a 2x2 filter and stride of 2
  # Input Tensor Shape: [batch_size, 28, 28, 32]
  # Output Tensor Shape: [batch_size, 14, 14, 32]
  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
  # Convolutional Layer #2
  # Computes 64 features using a 5x5 filter.
  # Padding is added to preserve width and height.
  # Input Tensor Shape: [batch_size, 14, 14, 32]
  # Output Tensor Shape: [batch_size, 14, 14, 64]
  conv2 = tf.layers.conv2d(
      inputs=pool1,
      filters=64,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)

  # Pooling Layer #2
  # Second max pooling layer with a 2x2 filter and stride of 2
  # Input Tensor Shape: [batch_size, 14, 14, 64]
  # Output Tensor Shape: [batch_size, 7, 7, 64]
  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

  # Flatten tensor into a batch of vectors
  # Input Tensor Shape: [batch_size, 7, 7, 64]
  # Output Tensor Shape: [batch_size, 7 * 7 * 64]
  pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])

  # Dense Layer
  # Densely connected layer with 1024 neurons
  # Input Tensor Shape: [batch_size, 7 * 7 * 64]
  # Output Tensor Shape: [batch_size, 1024]
  dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)

  # Add dropout operation; 0.6 probability that element will be kept
  dropout = tf.layers.dropout(
      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

  # Logits layer
  # Input Tensor Shape: [batch_size, 1024]
  # Output Tensor Shape: [batch_size, 10]
  return tf.layers.dense(inputs=dropout, units=10)


def cnn_model_fn(features, labels, mode, params):
    """Model function for CNN."""
    # Input Layer
    # Reshape X to 4-D tensor: [batch_size, width, height, channels]
    # MNIST images are 28x28 pixels, and have one color channel
    logits = create_model(features, mode)  # PREDICT
    predictions = {
          # Generate predictions (for PREDICT and EVAL mode)
          "classes": tf.argmax(input=logits, axis=1),
          # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
          # `logging_hook`.
          "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    # Calculate Loss (for both TRAIN and EVAL modes)
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    tf.summary.scalar('loss', loss)
    # Configure the Training Op (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=FLAGS.learning_rate)
        distributedOptimizer = NPUDistributedOptimizer(optimizer)
        train_op = distributedOptimizer.minimize(loss, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
    # Add evaluation metrics (for EVAL mode)
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels, predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


def train_input_fn(train_data, train_labels):
    return tf.estimator.inputs.numpy_input_fn(
        x={"x": train_data},
        y=train_labels,
        batch_size=FLAGS.batch_size,
        num_epochs=None,
        shuffle=True)

def eval_input_fn(eval_data, eval_labels):
    return tf.estimator.inputs.numpy_input_fn(
        x={"x": eval_data},
        y=eval_labels,
        num_epochs=1,
        shuffle=False)

def set_log():
  # get TF logger
  log = logging.getLogger('tensorflow')
  log.setLevel(logging.INFO)

  # create formatter and add it to the handlers
  formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

  # create file handler which logs even debug messages
  fh = logging.FileHandler(FLAGS.local_log_dir)
  fh.setLevel(logging.INFO)
  fh.setFormatter(formatter)
  log.addHandler(fh)


def main(unused_argv):
    # set the log
    set_log()

    # Load training and eval data
    mnist = input_data.read_data_sets(FLAGS.data_dir, False)
    train_data = mnist.train.images # Returns np.array
    train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
    eval_data = mnist.test.images # Returns np.array
    eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

    npu_config = NPURunConfig(
        iterations_per_loop = 100,
        save_checkpoints_steps=10,
        model_dir = FLAGS.model_dir,
        session_config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
    )

    mnist_classifier = NPUEstimator(
        model_fn=cnn_model_fn,
        config=npu_config,
        params = {},
        job_start_file=FLAGS.job_start_file
    )

    # Set up logging for predictions
    # Log the values in the "Softmax" tensor with label "probabilities"
    tensors_to_log = {"probabilities": "softmax_tensor"}
    logging_hook = tf.train.LoggingTensorHook(
    tensors=tensors_to_log, every_n_iter=50)  # Train the model

    mnist_classifier.train(
        input_fn=train_input_fn(train_data,train_labels),
        steps=FLAGS.train_steps, hooks=[logging_hook])

    # Evaluate the model and print results
    print("Evaluate the model...")
    eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn(eval_data,eval_labels), steps=FLAGS.eval_steps)
    print("eval_results: ", eval_results)

if __name__ == "__main__":
    tf.app.run()
